{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "20d1844d-56a1-4d97-9098-04cd38614402",
   "metadata": {},
   "source": [
    "# Datetime features to capture seasonality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c7e22b9-366f-4754-ba9c-c642f5b6f291",
   "metadata": {},
   "source": [
    "[Feature Engineering for Time Series Forecasting](https://www.trainindata.com/p/feature-engineering-for-forecasting)\n",
    "\n",
    "In this notebook we will show how to create features from the calendar and time (e.g, day of the week, month of the year, hour of day) to help capture seasonality. We will also show how we can use these features when creating a forecast.\n",
    "\n",
    "\n",
    "This is a prerequisite for creating seasonal dummy features, shown in the next lecture.\n",
    "\n",
    "\n",
    "Later in the course, we have a whole section dedicated to extracting features from date and time where we cover this topic in even more depth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a86e7d9-212c-4d82-b896-20a4e078c336",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import re\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context(\"talk\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e964ccb-f3fd-4e88-a08a-b82e6fc56074",
   "metadata": {},
   "source": [
    "# Data set synopsis\n",
    "\n",
    "We will use the Victoria electricity demand dataset found here: \n",
    "https://github.com/tidyverts/tsibbledata/tree/master/data-raw/vic_elec. This dataset is used in the [original MSTL paper [1]](https://arxiv.org/pdf/2107.13462.pdf). It is the total electricity demand at a half hourly granularity for the state of Victora in Australia from 2002 to the start of 2015. A more detailed description of the dataset can be found [here](https://rdrr.io/cran/tsibbledata/man/vic_elec.html). \n",
    "\n",
    "We resampled the dataset to hourly in the 4th data preparation notebook in the \"01-Create-Datasets\" folder in this repo. For instructions on how to download, prepare, and store the dataset, refer to notebook number 4, in the folder \"01-Create-Datasets\" from this repo.\n",
    "\n",
    "## References\n",
    "[1] [K. Bandura, R.J. Hyndman, and C. Bergmeir (2021)\n",
    "    MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple\n",
    "    Seasonal Patterns. arXiv preprint arXiv:2107.13462.](https://arxiv.org/pdf/2107.13462.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "969cc82e-0ed6-481b-b50a-74d0df94aa52",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9a8af020-f4e4-4bce-8dea-e4e7d57819a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\n",
    "    \"../Datasets/victoria_electricity_demand.csv\",\n",
    "    usecols=[\"demand\", \"date_time\"],\n",
    "    parse_dates=[\"date_time\"],\n",
    "    index_col=[\"date_time\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a1c7c23-5218-4da0-91bb-c94c68859c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(115368, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e60cdf1a-6461-4fb3-ba03-ba6227195790",
   "metadata": {},
   "source": [
    "The data is relatively large compared to the other datasets we have been working with. If the rest of this notebook runs too slowly try filtering to a recent segment of the data. For example by running:\n",
    "\n",
    "```Python\n",
    "# Filter to the previous 3 years of the dataset\n",
    "data = data.loc[\"2012\":]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea3cc441-df77-439b-a88d-e74c334635c3",
   "metadata": {},
   "source": [
    "## Plot the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6263c527-b50a-4998-a4aa-79b05ed40cc2",
   "metadata": {},
   "source": [
    "There time series is high frequency and over a long period. Let's plot the previous three years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e096e012-faba-477d-b0b3-6d2a2aac6407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[20, 5])\n",
    "data.loc[\"2012\":].plot(y=\"demand\", legend=None, ax=ax)\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Electricity Demand\")\n",
    "ax.set_title(\"Electricity Demand\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2683607-c1ab-4b60-b700-05ae7d36f105",
   "metadata": {},
   "source": [
    "# Creating datetime features with sktime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2583f4c-ba76-4ca2-bcff-49a67dd446ab",
   "metadata": {},
   "source": [
    "Datetime features are covered in depth in the `Time Features` section of the course. In that section we show how to create these features in `Pandas` and `Feature-engine`. In this section we show how to create these features using the `DateTimeFeatures` transformer in `sktime`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2cf14b06-1206-4217-9ba2-559739d130b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.transformations.series.date import DateTimeFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38419a54-8300-4934-860a-a590b045dcbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>demand</th>\n",
       "      <th>year</th>\n",
       "      <th>quarter_of_year</th>\n",
       "      <th>month_of_year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month_of_quarter</th>\n",
       "      <th>week_of_quarter</th>\n",
       "      <th>day_of_quarter</th>\n",
       "      <th>week_of_month</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>is_weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-01-01 00:00:00</th>\n",
       "      <td>6919.366092</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 01:00:00</th>\n",
       "      <td>7165.974188</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 02:00:00</th>\n",
       "      <td>6406.542994</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 03:00:00</th>\n",
       "      <td>5815.537828</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 04:00:00</th>\n",
       "      <td>5497.732922</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          demand  year  quarter_of_year  month_of_year  \\\n",
       "date_time                                                                \n",
       "2002-01-01 00:00:00  6919.366092  2002                1              1   \n",
       "2002-01-01 01:00:00  7165.974188  2002                1              1   \n",
       "2002-01-01 02:00:00  6406.542994  2002                1              1   \n",
       "2002-01-01 03:00:00  5815.537828  2002                1              1   \n",
       "2002-01-01 04:00:00  5497.732922  2002                1              1   \n",
       "\n",
       "                     week_of_year  day_of_year  month_of_quarter  \\\n",
       "date_time                                                          \n",
       "2002-01-01 00:00:00             1            1                 1   \n",
       "2002-01-01 01:00:00             1            1                 1   \n",
       "2002-01-01 02:00:00             1            1                 1   \n",
       "2002-01-01 03:00:00             1            1                 1   \n",
       "2002-01-01 04:00:00             1            1                 1   \n",
       "\n",
       "                     week_of_quarter  day_of_quarter  week_of_month  \\\n",
       "date_time                                                             \n",
       "2002-01-01 00:00:00                2               1              1   \n",
       "2002-01-01 01:00:00                2               1              1   \n",
       "2002-01-01 02:00:00                2               1              1   \n",
       "2002-01-01 03:00:00                2               1              1   \n",
       "2002-01-01 04:00:00                2               1              1   \n",
       "\n",
       "                     day_of_month  day_of_week  hour_of_day  is_weekend  \n",
       "date_time                                                                \n",
       "2002-01-01 00:00:00             1            1            0           0  \n",
       "2002-01-01 01:00:00             1            1            1           0  \n",
       "2002-01-01 02:00:00             1            1            2           0  \n",
       "2002-01-01 03:00:00             1            1            3           0  \n",
       "2002-01-01 04:00:00             1            1            4           0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer = DateTimeFeatures(ts_freq=\"H\", # Frequency of the time series\n",
    "                               feature_scope=\"comprehensive\", # Can be \"comprehensive\", \n",
    "                                                              # \"efficient\", \"minimal\".\n",
    "                                                              # Depending on the time series\n",
    "                                                              # frequency a sensible set is \n",
    "                                                              # automatically chosen.\n",
    "                               keep_original_columns=True, # Flag if we want to keep columns\n",
    "                                                           # in the dataframe passed to `transform`.\n",
    "                              )\n",
    "\n",
    "transformer.fit(data) # This transformer has a fit method which does\n",
    "                      # not learn any parameters. sktime still  \n",
    "                      # performs a series of checks when we call `fit` on the time series \n",
    "                      # (e.g., is the time series sorted?, are there missing timestamps?).\n",
    "            \n",
    "result = transformer.transform(data) # Create the features from the datetime index. \n",
    "result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbcbc5b6-6d9a-4d39-84c0-9d66559ba97a",
   "metadata": {},
   "source": [
    "We can also manually select the date time features we want to create using the `manual_selection` argument. \n",
    "\n",
    "The valid set of values for `manual_selection` for the most recent version of sktime during the creation of this notebook (i.e., version 0.18.1) is:\n",
    "- year \n",
    "- quarter_of_year \n",
    "- month_of_year \n",
    "- week_of_year \n",
    "- day_of_year \n",
    "- month_of_quarter \n",
    "- week_of_quarter \n",
    "- day_of_quarter \n",
    "- week_of_month \n",
    "- day_of_month \n",
    "- day_of_week \n",
    "- hour_of_day \n",
    "- minute_of_hour \n",
    "- second_of_minute \n",
    "- millisecond_of_second \n",
    "- is_weekend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c82f5c06-fb56-4a0e-ab0f-03335eccd0a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>demand</th>\n",
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       "      <th>2002-01-01 00:00:00</th>\n",
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       "      <th>2002-01-01 03:00:00</th>\n",
       "      <td>5815.537828</td>\n",
       "      <td>2002</td>\n",
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       "      <td>1</td>\n",
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       "      <td>5497.732922</td>\n",
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       "      <th>2015-02-28 19:00:00</th>\n",
       "      <td>9596.777060</td>\n",
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       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>59</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
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       "      <th>2015-02-28 20:00:00</th>\n",
       "      <td>8883.230296</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>59</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-28 21:00:00</th>\n",
       "      <td>8320.260550</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>59</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-28 22:00:00</th>\n",
       "      <td>8110.055916</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>59</td>\n",
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       "<p>115368 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          demand  year  month_of_year  week_of_year  \\\n",
       "date_time                                                             \n",
       "2002-01-01 00:00:00  6919.366092  2002              1             1   \n",
       "2002-01-01 01:00:00  7165.974188  2002              1             1   \n",
       "2002-01-01 02:00:00  6406.542994  2002              1             1   \n",
       "2002-01-01 03:00:00  5815.537828  2002              1             1   \n",
       "2002-01-01 04:00:00  5497.732922  2002              1             1   \n",
       "...                          ...   ...            ...           ...   \n",
       "2015-02-28 19:00:00  9596.777060  2015              2             9   \n",
       "2015-02-28 20:00:00  8883.230296  2015              2             9   \n",
       "2015-02-28 21:00:00  8320.260550  2015              2             9   \n",
       "2015-02-28 22:00:00  8110.055916  2015              2             9   \n",
       "2015-02-28 23:00:00  8519.368752  2015              2             9   \n",
       "\n",
       "                     day_of_year  day_of_week  hour_of_day  is_weekend  \n",
       "date_time                                                               \n",
       "2002-01-01 00:00:00            1            1            0           0  \n",
       "2002-01-01 01:00:00            1            1            1           0  \n",
       "2002-01-01 02:00:00            1            1            2           0  \n",
       "2002-01-01 03:00:00            1            1            3           0  \n",
       "2002-01-01 04:00:00            1            1            4           0  \n",
       "...                          ...          ...          ...         ...  \n",
       "2015-02-28 19:00:00           59            5           19           1  \n",
       "2015-02-28 20:00:00           59            5           20           1  \n",
       "2015-02-28 21:00:00           59            5           21           1  \n",
       "2015-02-28 22:00:00           59            5           22           1  \n",
       "2015-02-28 23:00:00           59            5           23           1  \n",
       "\n",
       "[115368 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify which datetime features to create\n",
    "datetime_features = [\n",
    "                       \"year\",\n",
    "                       \"month_of_year\",\n",
    "                       \"week_of_year\",\n",
    "                       \"day_of_year\",\n",
    "                       \"day_of_week\",\n",
    "                       \"hour_of_day\",\n",
    "                       \"is_weekend\",\n",
    "                    ]\n",
    "\n",
    "# Create the DateTimeFeatures transformer\n",
    "transformer = DateTimeFeatures( # We do not need to specify the frequency of the time series\n",
    "                                # if we are manually selecting which features to create.\n",
    "                               manual_selection=datetime_features, # Select which features to\n",
    "                                                                   # create.\n",
    "                               keep_original_columns=True, # Flag if we want to keep columns\n",
    "                                                            # in dataframe passed to `transform`.\n",
    "                              )\n",
    "\n",
    "# Fit and transform to create our features\n",
    "result = transformer.fit_transform(data)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7988f5dc-9504-4a0c-a818-d1c4d3f7fdd4",
   "metadata": {},
   "source": [
    "# Plots to understand seasonality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24f2b7c-4c4c-4d09-8e21-ec638f841d54",
   "metadata": {},
   "source": [
    "Let's quickly look at the average by different periods (daily, weekly, yearly, etc.) to remind ourselves of the seasonality. There isn't much trend in this data so we won't be too concerned about not de-trending the data first."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c39a20-a96d-4f2d-be18-5ad585de79f7",
   "metadata": {},
   "source": [
    "For a full exploratory data analysis of seasonality in this dataset see notebook `06-MSTL-decomposition.ipynb` in the `04-Time-Series-Decomposition` section of this repo. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf100935-6261-46f7-a8a7-95833ee284f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mean electricity demand vs hour of day')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot average demand by hour of day\n",
    "# to understand daily seasonality. \n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "sns.barplot(data=result, y=\"demand\", x=\"hour_of_day\", ax=ax, color='b', alpha=0.25)\n",
    "ax.set_title(\"Mean electricity demand vs hour of day\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41a405bb-5718-4b66-ae36-6ab9f91a0d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='day_of_week', ylabel='demand'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot average demand by day of week\n",
    "# to understand weekly seasonality. \n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "sns.barplot(data=result, \n",
    "            y=\"demand\",\n",
    "            x=\"day_of_week\",\n",
    "            ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3b7151c6-f45f-47c3-85ac-ac3a56442221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='month_of_year', ylabel='demand'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot average demand by month of year\n",
    "# to understand yearly seasonality. \n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "sns.barplot(data=result, y=\"demand\", x=\"month_of_year\", ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d20fba2-1d7b-472c-a73a-d67e55173bb7",
   "metadata": {},
   "source": [
    "# Forecasting with datetime features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e835157-d0e9-4571-81dc-6fa818ffb032",
   "metadata": {},
   "source": [
    "Let's build a recursive forecast and see how we can include datetime features in our feature engineering pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "738606c1-df44-4f9f-b4d7-4e5a1f22d234",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Date time features to capture seasonality\n",
    "from sktime.transformations.series.date import DateTimeFeatures\n",
    "\n",
    "# --- The transformers from earlier in the course. --- #\n",
    "# Lag and window features\n",
    "from sktime.transformations.series.summarize import WindowSummarizer\n",
    "# Time features for trend \n",
    "from sktime.transformations.series.time_since import TimeSince\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "# Rescaling transformer for linear models with regularisation\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# Pipelines to create feature engineering pipeline\n",
    "from sklearn.pipeline import make_pipeline, make_union\n",
    "# Used to reset sklearn estimators\n",
    "from sklearn.base import clone\n",
    "\n",
    "# Let's ensure all sklearn transformers output pandas dataframes\n",
    "from sklearn import set_config\n",
    "set_config(transform_output=\"pandas\")  # Upgrade to scikit-learn >= 0.12\n",
    "                                       # for this feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "130d5c18-2870-4a1a-89af-c09bdff6b775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>demand</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-01-01 00:00:00</th>\n",
       "      <td>6919.366092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 01:00:00</th>\n",
       "      <td>7165.974188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 02:00:00</th>\n",
       "      <td>6406.542994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 03:00:00</th>\n",
       "      <td>5815.537828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 04:00:00</th>\n",
       "      <td>5497.732922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          demand\n",
       "date_time                       \n",
       "2002-01-01 00:00:00  6919.366092\n",
       "2002-01-01 01:00:00  7165.974188\n",
       "2002-01-01 02:00:00  6406.542994\n",
       "2002-01-01 03:00:00  5815.537828\n",
       "2002-01-01 04:00:00  5497.732922"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d292e0e-e5ce-4c5d-b63a-6490a6457b6b",
   "metadata": {},
   "source": [
    "Specify target name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "25d2262f-c242-409b-93b4-0913f70c3113",
   "metadata": {},
   "outputs": [],
   "source": [
    "target=[\"demand\"] # Note: it's in a list.\n",
    "                  # This ensures we'll get\n",
    "                  # a dataframe when using df.loc[:, target]\n",
    "                  # rather than a pandas Series. \n",
    "                  # This can also be useful if we have\n",
    "                  # multiple targets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dac1165-9d17-4648-b7ed-927ed660de3d",
   "metadata": {},
   "source": [
    "Prepare our transformers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "002c3527-412a-4dc4-b359-1c829a8a4789",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Polynomial time features for trend\n",
    "time_feats = make_pipeline(\n",
    "                           TimeSince(), \n",
    "                           PolynomialFeatures(degree=1, include_bias=False)\n",
    "                          )\n",
    "\n",
    "# Datetime features\n",
    "# Specify which datetime features to create\n",
    "datetime_features = [\n",
    "                       \"month_of_year\",\n",
    "                       \"week_of_year\",\n",
    "                       \"day_of_year\",\n",
    "                       \"day_of_week\",\n",
    "                       \"hour_of_day\",\n",
    "                    ]\n",
    "\n",
    "datetime_feats = DateTimeFeatures(\n",
    "                               manual_selection=datetime_features, \n",
    "                               keep_original_columns=False, \n",
    "                              )\n",
    "\n",
    "\n",
    "\n",
    "# Compute lag and window features.\n",
    "lag_window_feats = WindowSummarizer(\n",
    "    lag_feature={\n",
    "        \"lag\": [1,2,3,4] # np.arange(1, 25),  # Lag features.\n",
    "#        \"mean\": [[1, 24], [1, 24*7]],  # [[lag, window size]]\n",
    "    },\n",
    "    target_cols=target,\n",
    "    truncate=\"bfill\",  # Backfill missing values from lagging and windowing.\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d0525e4-b9a3-4977-88e6-cc213e4d8a9e",
   "metadata": {},
   "source": [
    "Create a pipeline to create all our features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8224a415-81da-4471-9e65-53f0ed4b7ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To see how the datetime features help\n",
    "# try commenting them out and just using\n",
    "# lag features, then try just using datetime\n",
    "# features.\n",
    "pipeline = make_union(\n",
    "    datetime_feats,\n",
    "    time_feats, \n",
    "    lag_window_feats,\n",
    ")\n",
    "\n",
    "# Apply min-max scaling to all the features\n",
    "pipeline = make_pipeline(pipeline, MinMaxScaler())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b74e1d09-c547-42dc-b736-f3e55d756749",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;featureunion&#x27;,\n",
       "                 FeatureUnion(transformer_list=[(&#x27;datetimefeatures&#x27;,\n",
       "                                                 DateTimeFeatures(manual_selection=[&#x27;month_of_year&#x27;,\n",
       "                                                                                    &#x27;week_of_year&#x27;,\n",
       "                                                                                    &#x27;day_of_year&#x27;,\n",
       "                                                                                    &#x27;day_of_week&#x27;,\n",
       "                                                                                    &#x27;hour_of_day&#x27;])),\n",
       "                                                (&#x27;pipeline&#x27;,\n",
       "                                                 Pipeline(steps=[(&#x27;timesince&#x27;,\n",
       "                                                                  TimeSince()),\n",
       "                                                                 (&#x27;polynomialfeatures&#x27;,\n",
       "                                                                  PolynomialFeatures(degree=1,\n",
       "                                                                                     include_bias=False))])),\n",
       "                                                (&#x27;windowsummarizer&#x27;,\n",
       "                                                 WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1,\n",
       "                                                                                       2,\n",
       "                                                                                       3,\n",
       "                                                                                       4]},\n",
       "                                                                  target_cols=[&#x27;demand&#x27;],\n",
       "                                                                  truncate=&#x27;bfill&#x27;))])),\n",
       "                (&#x27;minmaxscaler&#x27;, MinMaxScaler())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;featureunion&#x27;,\n",
       "                 FeatureUnion(transformer_list=[(&#x27;datetimefeatures&#x27;,\n",
       "                                                 DateTimeFeatures(manual_selection=[&#x27;month_of_year&#x27;,\n",
       "                                                                                    &#x27;week_of_year&#x27;,\n",
       "                                                                                    &#x27;day_of_year&#x27;,\n",
       "                                                                                    &#x27;day_of_week&#x27;,\n",
       "                                                                                    &#x27;hour_of_day&#x27;])),\n",
       "                                                (&#x27;pipeline&#x27;,\n",
       "                                                 Pipeline(steps=[(&#x27;timesince&#x27;,\n",
       "                                                                  TimeSince()),\n",
       "                                                                 (&#x27;polynomialfeatures&#x27;,\n",
       "                                                                  PolynomialFeatures(degree=1,\n",
       "                                                                                     include_bias=False))])),\n",
       "                                                (&#x27;windowsummarizer&#x27;,\n",
       "                                                 WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1,\n",
       "                                                                                       2,\n",
       "                                                                                       3,\n",
       "                                                                                       4]},\n",
       "                                                                  target_cols=[&#x27;demand&#x27;],\n",
       "                                                                  truncate=&#x27;bfill&#x27;))])),\n",
       "                (&#x27;minmaxscaler&#x27;, MinMaxScaler())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">featureunion: FeatureUnion</label><div class=\"sk-toggleable__content\"><pre>FeatureUnion(transformer_list=[(&#x27;datetimefeatures&#x27;,\n",
       "                                DateTimeFeatures(manual_selection=[&#x27;month_of_year&#x27;,\n",
       "                                                                   &#x27;week_of_year&#x27;,\n",
       "                                                                   &#x27;day_of_year&#x27;,\n",
       "                                                                   &#x27;day_of_week&#x27;,\n",
       "                                                                   &#x27;hour_of_day&#x27;])),\n",
       "                               (&#x27;pipeline&#x27;,\n",
       "                                Pipeline(steps=[(&#x27;timesince&#x27;, TimeSince()),\n",
       "                                                (&#x27;polynomialfeatures&#x27;,\n",
       "                                                 PolynomialFeatures(degree=1,\n",
       "                                                                    include_bias=False))])),\n",
       "                               (&#x27;windowsummarizer&#x27;,\n",
       "                                WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1, 2, 3,\n",
       "                                                                      4]},\n",
       "                                                 target_cols=[&#x27;demand&#x27;],\n",
       "                                                 truncate=&#x27;bfill&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>datetimefeatures</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DateTimeFeatures</label><div class=\"sk-toggleable__content\"><pre>DateTimeFeatures(manual_selection=[&#x27;month_of_year&#x27;, &#x27;week_of_year&#x27;,\n",
       "                                   &#x27;day_of_year&#x27;, &#x27;day_of_week&#x27;,\n",
       "                                   &#x27;hour_of_day&#x27;])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>pipeline</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TimeSince</label><div class=\"sk-toggleable__content\"><pre>TimeSince()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(degree=1, include_bias=False)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>windowsummarizer</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">WindowSummarizer</label><div class=\"sk-toggleable__content\"><pre>WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1, 2, 3, 4]}, target_cols=[&#x27;demand&#x27;],\n",
       "                 truncate=&#x27;bfill&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('featureunion',\n",
       "                 FeatureUnion(transformer_list=[('datetimefeatures',\n",
       "                                                 DateTimeFeatures(manual_selection=['month_of_year',\n",
       "                                                                                    'week_of_year',\n",
       "                                                                                    'day_of_year',\n",
       "                                                                                    'day_of_week',\n",
       "                                                                                    'hour_of_day'])),\n",
       "                                                ('pipeline',\n",
       "                                                 Pipeline(steps=[('timesince',\n",
       "                                                                  TimeSince()),\n",
       "                                                                 ('polynomialfeatures',\n",
       "                                                                  PolynomialFeatures(degree=1,\n",
       "                                                                                     include_bias=False))])),\n",
       "                                                ('windowsummarizer',\n",
       "                                                 WindowSummarizer(lag_feature={'lag': [1,\n",
       "                                                                                       2,\n",
       "                                                                                       3,\n",
       "                                                                                       4]},\n",
       "                                                                  target_cols=['demand'],\n",
       "                                                                  truncate='bfill'))])),\n",
       "                ('minmaxscaler', MinMaxScaler())])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6decf02c-1a0a-45d0-9fec-07255f232a17",
   "metadata": {},
   "source": [
    "Let's check how our feature engineering pipeline behaves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8071a5af-cfb4-4a2b-85d8-d0e065f53fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month_of_year</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>time_since_2002-01-01 00:00:00</th>\n",
       "      <th>demand_lag_1</th>\n",
       "      <th>demand_lag_2</th>\n",
       "      <th>demand_lag_3</th>\n",
       "      <th>demand_lag_4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-01-01 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.817386</td>\n",
       "      <td>0.675273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 01:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.817386</td>\n",
       "      <td>0.675273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 02:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.675273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 03:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.437640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01 04:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     month_of_year  week_of_year  day_of_year  day_of_week  \\\n",
       "date_time                                                                    \n",
       "2002-01-01 00:00:00            0.0           0.0          0.0          0.0   \n",
       "2002-01-01 01:00:00            0.0           0.0          0.0          0.0   \n",
       "2002-01-01 02:00:00            0.0           0.0          0.0          0.0   \n",
       "2002-01-01 03:00:00            0.0           0.0          0.0          0.0   \n",
       "2002-01-01 04:00:00            0.0           0.0          0.0          0.0   \n",
       "\n",
       "                     hour_of_day  time_since_2002-01-01 00:00:00  \\\n",
       "date_time                                                          \n",
       "2002-01-01 00:00:00         0.00                            0.00   \n",
       "2002-01-01 01:00:00         0.25                            0.25   \n",
       "2002-01-01 02:00:00         0.50                            0.50   \n",
       "2002-01-01 03:00:00         0.75                            0.75   \n",
       "2002-01-01 04:00:00         1.00                            1.00   \n",
       "\n",
       "                     demand_lag_1  demand_lag_2  demand_lag_3  demand_lag_4  \n",
       "date_time                                                                    \n",
       "2002-01-01 00:00:00      0.817386      0.675273           0.0           0.0  \n",
       "2002-01-01 01:00:00      0.817386      0.675273           0.0           0.0  \n",
       "2002-01-01 02:00:00      1.000000      0.675273           0.0           0.0  \n",
       "2002-01-01 03:00:00      0.437640      1.000000           0.0           0.0  \n",
       "2002-01-01 04:00:00      0.000000      0.000000           1.0           0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit_transform(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf8a062-c2be-4d22-9b1f-73be7f52b4f0",
   "metadata": {},
   "source": [
    "Let's reset our feature engineering pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "22ad95c0-b937-471c-a2ae-fe17ed2d3f2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can use `clone` to return an unfitted version\n",
    "# of the pipeline.\n",
    "pipeline = clone(pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9445dbf-0c67-4e52-9df7-ffc5ff178ef6",
   "metadata": {},
   "source": [
    "Let's build a recursive forecast."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c79eb0e-d6e1-40eb-a930-80900822f66f",
   "metadata": {},
   "source": [
    "We'll start with configuring the model, the forecast start time, the number of steps to forecast, and the forecasting horizon, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3e812426-089d-4add-bbb7-8a630e879efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from lightgbm import LGBMRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fee3871f-b8ca-4063-9272-37c0f71f626a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- CONFIG --- #\n",
    "# Define time of first forecast, this determines our train / test split.                                              \n",
    "forecast_start_time = df.index.max() - pd.DateOffset(weeks=2) # Start two weeks from the end.\n",
    "\n",
    "# Define number of steps to forecast.\n",
    "num_of_forecast_steps = 24*14 # 2 weeks, this is a lot!\n",
    "\n",
    "# Define the model.\n",
    "model = LGBMRegressor(\n",
    "                      boosting = \"gbdt\",\n",
    "                      learning_rate=0.1,\n",
    "                      n_estimators=100,\n",
    "                      n_jobs=-1, # Use all cores\n",
    "                     )\n",
    "\n",
    "# Create a list of periods that we'll forecast over.\n",
    "forecast_horizon = pd.date_range(forecast_start_time, \n",
    "                                   periods=num_of_forecast_steps,\n",
    "                                   freq=\"H\")\n",
    "\n",
    "# How much data in the past is needed to create our features\n",
    "look_back_window_size = pd.DateOffset(weeks=1) # We need the latest 24*7 time periods\n",
    "                                             # in our predict dataframe to build our \n",
    "                                             # window features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ffaef60-a54d-4d0c-a19b-e4e5202ec896",
   "metadata": {},
   "source": [
    "Let's create our training dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e25af278-6269-4c5f-8c8c-7ff0cd1c01fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- CREATE TRAINING & TESTING DATAFRAME  --- #\n",
    "# Ensure we only have training data up to the start\n",
    "# of the forecast.\n",
    "df_train = df.loc[df.index < forecast_start_time].copy()\n",
    "df_test = df.loc[df.index >= forecast_start_time].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb9e82a-2228-40a7-a73b-9b7a31391cf0",
   "metadata": {},
   "source": [
    "Let's compute our `X_train` and `y_train` and fit our model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "528ab868-ea31-40cf-aafb-410e29fe9eb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] boosting is set=gbdt, boosting_type=gbdt will be ignored. Current value: boosting=gbdt\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(boosting=&#x27;gbdt&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(boosting=&#x27;gbdt&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMRegressor(boosting='gbdt')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- FEATURE ENGINEERING--- #\n",
    "# Create X_train and y_train\n",
    "y_train = df_train[target]\n",
    "X_train = pipeline.fit_transform(df_train)\n",
    "\n",
    "# LightGBM cannot handle column names which have\n",
    "# certain characters (e.g., \":\"). We replace these\n",
    "# with `_`. \n",
    "if \"lightgbm\" in model.__module__: # checks if model is from lightgbm\n",
    "    X_train = X_train.rename(columns = lambda x:re.sub(\"[^A-Za-z0-9_]+\", \"_\", x))\n",
    "\n",
    "\n",
    "# --- MODEL TRAINING---#\n",
    "# Train one-step ahead forecast model\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e5fd0c-89df-4ae0-ba7c-fb95902ff7d8",
   "metadata": {},
   "source": [
    "Let's prepare the dataframe that we will pass to `model.predict()`. This will contain some portion of time series during the training period so we can create any features that require historic data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d80416ff-ee17-4e88-a715-86f2a49fb40e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- CREATE DYNAMIC PREDICTION DATAFRAME --- #\n",
    "# We will recursively append our forecasts to this \n",
    "# dataframe and re-compute our lag and window features from the\n",
    "# target in this dataframe. It contains data in both the training period \n",
    "# and forecast period which is needed for some transformers (e.g., lags and windows).\n",
    "look_back_start_time = forecast_start_time - look_back_window_size\n",
    "\n",
    "# Create `df_predict` which has data going as far back\n",
    "# as needed to create features which need past values.\n",
    "df_predict = df_train.loc[look_back_start_time:].copy()\n",
    "\n",
    "# Extend index into forecast horizon\n",
    "df_predict = pd.concat([\n",
    "                  df_predict,\n",
    "                  pd.DataFrame(index=forecast_horizon)\n",
    "                 ])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65945f51-985d-46e3-a137-21cd2965312e",
   "metadata": {},
   "source": [
    "Let's recursively create `X_test` and make our predictions and append them to the `df_predict` dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d629a47f-6f84-4dea-8a53-4e7c4d1fae9c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# --- RECURSIVE FORECASTING LOOP --- #\n",
    "for forecast_time in forecast_horizon:    \n",
    "    # Compute features during the forecast horizon\n",
    "    X_test = pipeline.transform(df_predict)\n",
    "    X_test = X_test.loc[[forecast_time]] \n",
    "\n",
    "    # Predict one step ahead. \n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Append forecast to the target variable columnn in our\n",
    "    # dynamic forecast dataframe `df_predict`. This `df_predict`\n",
    "    # is ready for the next iteration where we will re-compute\n",
    "    # features derived from the target such as lags and windows.\n",
    "    df_predict.loc[[forecast_time], target] = y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b746cd-135f-46b4-8929-4b7e9ecd391d",
   "metadata": {},
   "source": [
    "Let's retrieve our forecast and actuals during the forest horizon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7931aa06-0468-41dc-b8a0-43dca249500d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- GET FORECAST AND TEST VALUES --- #    \n",
    "y_forecast = df_predict.loc[forecast_horizon, target]\n",
    "y_test = df_test.loc[forecast_start_time:, target]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c65617-d15a-48ad-b326-faa79c51b2f8",
   "metadata": {},
   "source": [
    "Let's create predictions on the training set using our one step ahead forecast model. This is useful to plot when debugging models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3b511c05-ce4e-449d-b849-2ab556d681d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- CREATE IN-SAMPLE PREDICTIONS --- #\n",
    "y_forecast_train = model.predict(X_train)\n",
    "y_forecast_train = pd.DataFrame(y_forecast_train, index=X_train.index, columns=target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86ba2671-7108-4a0d-a696-ba88fc844cf1",
   "metadata": {},
   "source": [
    "Let's plot the forecast!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ca0a3741-e4ec-4811-9e64-ce682b83a30f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Recursive forecast with LGBMRegressor(boosting='gbdt')\")"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- PLOTTING --- #\n",
    "# Plot the forecast.\n",
    "fig, ax = plt.subplots(figsize=[12, 7])\n",
    "\n",
    "# Plot training set.\n",
    "y_train.plot(ax=ax, marker='.')\n",
    "# Plot actuals in forecasting horizon.\n",
    "y_test.plot(ax=ax, marker='.', alpha=0.6)\n",
    "# Plot forecast.\n",
    "y_forecast.plot(ax=ax)\n",
    "# Plot 1 step forecasts in training data.\n",
    "y_forecast_train.plot(ax=ax)\n",
    "\n",
    "ax.legend([\"train\", \"test\", \"forecast\", \"in-sample forecast\"])\n",
    "ax.set_xlim(xmin=y_train.index.max() - pd.DateOffset(weeks=3))\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(f\"{target}\")\n",
    "ax.set_title(f\"Recursive forecast with {model}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b329343e-5055-4a8a-903d-4ec0252239a4",
   "metadata": {},
   "source": [
    "Let's compute the RMSE of this forecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ca4d4411-a247-47cd-882c-bd67833b69f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1401.9957896569902"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute error metrics.\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y_true=y_test.loc[y_forecast.index],\n",
    "                   y_pred=y_forecast,\n",
    "                   squared=False)"
   ]
  },
  {
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   "id": "524094c3-1093-4fa3-bddc-d7672ee41a3f",
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    "Feel free to change the dates, try different models, and different features!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d649f5f-cfbb-43b8-83d1-b168514acb9b",
   "metadata": {},
   "source": [
    "In this notebook we have shown: 1) how to create features from date and time to help capture seasonality, 2) how to add these features to a forecasting pipeline."
   ]
  }
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